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Questions tagged [control-theory]

For questions related to control theory and its relation to reinforcement learning and other artificial intelligence sub-fields.

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24 votes
1 answer

When should I use Reinforcement Learning vs PID Control?

When designing solutions to problems such as the Lunar Lander on OpenAIGym, Reinforcement Learning is a tempting means of giving the agent adequate action control so as to successfully land. But ...
SeeDerekEngineer's user avatar
23 votes
2 answers

What is the difference between reinforcement learning and optimal control?

Coming from a process (optimal) control background, I have begun studying the field of deep reinforcement learning. Sutton & Barto (2015) state that particularly important (to the writing of the ...
Bionic Buffulo's user avatar
4 votes
1 answer

Is there any difference between a control and an action in reinforcement learning?

There are reinforcement learning papers (e.g. Metacontrol for Adaptive Imagination-Based Optimization) that use (apparently, interchangeably) the term control or action to refer to the effect of the ...
nbro's user avatar
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4 votes
1 answer

Are there examples of neural networks (used for control) implemented on a FPGA or on a neurochip?

Greetings to all respected colleagues! I want to consult on the use of FPGAs and neurochips. I plan to use it in my laboratory project for programming control systems on neural networks. In my work,...
dtn's user avatar
  • 239
4 votes
1 answer

Can OpenAI simulations be used in real world applications?

I know that classical control systems have been used to solve the problem of the inverted pendulum - inverted pendulum. But I've seen that people have also used machine learning techniques to solve ...
blazingcannon's user avatar
4 votes
1 answer

How do I solve this optimal control problem with reinforcement learning?

I am new to reinforcement learning. I would like to solve an optimal control problem with reinforcement learning. The objective is for a wolf to catch a rabbit. The wolf and the rabbit run on a ...
TFbie's user avatar
  • 73
3 votes
1 answer

What does a joint probability density function have to do with Stochastic Optimal Control and Reinforcement Learning?

I stumbled upon a job offer from a company that was looking for someone who was good with Reinforcement Learning (applied to finance) and something in their offer caught my eye. It goes something like ...
Metrician's user avatar
3 votes
0 answers

How to deal with approximate states when doing path planning?

If one is interested in implementing a path planning algorithm that is grid-based, one needs to consider the fact that your grid points will never represent the true state of the robot. How is this ...
FourierFlux's user avatar
2 votes
0 answers

How do self-driving cars perform lane changes?

I am a bit stuck trying to understand how a lane change is performed from an operational point of view. Let's assume a self-driving car uses an occupancy grid map for local planning, this map may even ...
Ivan Novikov's user avatar
1 vote
0 answers

Control algorithms when system dynamics are stochastic and/or unknown

I'm working on a traffic signal control problem, which I am currently approaching with Reinforcement Learning, but I want to try some other control algorithms. This is hard for me because we don't ...
Federico Taschin's user avatar
1 vote
0 answers

Solving the dead time problem for control using reinforcement learning

There are several occasion that reinforcement learning can be used as a control mean. The action is for example the set target temperature (which in many occasions change with time) and the state is ...
JianNius's user avatar
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0 votes
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Seeking methods to incorporate arbitrary actuator faults for Control Optimization

I am working on a problem where a control method, backed by a Neural Network (NN), dictates the movement of a 1D actuator to influence a specific process. This actuator can move linearly within a set ...
IsolatedSushi's user avatar